ARC-II. Models that defer the interaction between inputs until their individual representations “mature,” like ARC-I, run the risk of losing information that can be important for matching, because each representation is formed without knowledge of the others [1]}.
ARC-II [1]} addresses this problem by using interactions between query and document, so that the network gets the opportunity to capture various matching patterns between the input texts from the start. It learns directly from interactions rather than from individual representations. A first convolution layer creates combinations of the inputs via sliding windows on both sentences, so that the remaining layers can extract matching features.
